feature_table.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "# display all columns and rows\n",
    "pd.set_option('display.max_colwidth',-1)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of users: 70\n"
     ]
    }
   ],
   "source": [
    "users = [\n",
    "'u004_rct@eureka',\n",
    "'u005_rct@eureka',\n",
    "'u006_rct@eureka',\n",
    "'u007_rct@eureka',\n",
    "'u008_rct@eureka',\n",
    "'u009_rct@eureka',\n",
    "'u010_rct@eureka',\n",
    "'u011_rct@eureka',\n",
    "'u012_rct@eureka',\n",
    "'u013_rct@eureka',\n",
    "'u014_rct@eureka',\n",
    "'u015_rct@eureka',\n",
    "'u016_rct@eureka',\n",
    "'u017_rct@eureka',\n",
    "'u018_rct@eureka',\n",
    "'u019_rct@eureka',\n",
    "'u020_rct@eureka',\n",
    "'u021_rct@eureka',\n",
    "'u022_rct@eureka',\n",
    "'u023_rct@eureka',\n",
    "'u024_rct@eureka',\n",
    "'u025_rct@eureka',\n",
    "'u026_rct@eureka',\n",
    "'u027_rct@eureka',\n",
    "'u028_rct@eureka',\n",
    "'u029_rct@eureka',\n",
    "'u030_rct@eureka',\n",
    "'u031_rct@eureka',\n",
    "'u032_rct@eureka',\n",
    "'u033_rct@eureka',\n",
    "'u034_rct@eureka',\n",
    "'u035_rct@eureka',\n",
    "'u036_rct@eureka',\n",
    "'u037_rct@eureka',\n",
    "'u038_rct@eureka',\n",
    "'u039_rct@eureka',\n",
    "'u040_rct@eureka',\n",
    "'u041_rct@eureka',\n",
    "'u042_rct@eureka',\n",
    "'u043_rct@eureka',\n",
    "'u044_rct@eureka',\n",
    "'u045_rct@eureka',\n",
    "'u046_rct@eureka',\n",
    "'u047_rct@eureka',\n",
    "'u048_rct@eureka',\n",
    "'u049_rct@eureka',\n",
    "'u050_rct@eureka',\n",
    "'u051_rct@eureka',\n",
    "'u052_rct@eureka',\n",
    "'u053_rct@eureka',\n",
    "'u054_rct@eureka',\n",
    "'u055_rct@eureka',\n",
    "'u056_rct@eureka',\n",
    "'u057_rct@eureka',\n",
    "'u058_rct@eureka',\n",
    "'u059_rct@eureka',\n",
    "'u060_rct@eureka',\n",
    "'u061_rct@eureka',\n",
    "'u062_rct@eureka',\n",
    "'u063_rct@eureka',\n",
    "'u064_rct@eureka',\n",
    "'u065_rct@eureka',\n",
    "'u066_rct@eureka',\n",
    "'u067_rct@eureka',\n",
    "'u068_rct@eureka',\n",
    "'u069_rct@eureka',\n",
    "'u070_rct@eureka',\n",
    "'u071_rct@eureka',\n",
    "'u072_rct@eureka',\n",
    "'u073_rct@eureka',\n",
    "]\n",
    "print('total number of users:', len(users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-08-13 00:24:30.873490\n",
      "2020-08-13 00:31:57.684316\n"
     ]
    }
   ],
   "source": [
    "# get all processed feature tables\n",
    "def get_table(csv_file):    \n",
    "    csv_file = csv_file.drop(['Unnamed: 0'], axis=1)\n",
    "    csv_file['time'] = pd.to_datetime(csv_file['time'])\n",
    "    return csv_file\n",
    "\n",
    "print(datetime.datetime.now())\n",
    "calllog = get_table(pd.read_csv('table_calllog.csv'))\n",
    "smslog = get_table(pd.read_csv('table_sms.csv'))\n",
    "unlock = get_table(pd.read_csv('table_raw_unlock.csv'))\n",
    "conver = get_table(pd.read_csv('table_conver.csv'))\n",
    "sleep = get_table(pd.read_csv('table_sleep.csv'))\n",
    "act = get_table(pd.read_csv('table_act.csv'))\n",
    "light = get_table(pd.read_csv('table_light.csv'))\n",
    "audio_amp = get_table(pd.read_csv('table_audio_amp.csv'))\n",
    "audio_infer = get_table(pd.read_csv('table_audio_infer_new.csv'))\n",
    "print(datetime.datetime.now())\n",
    "\n",
    "ema = pd.read_csv('full_dataset.csv')\n",
    "ema = ema[['study_id','eureka_id','day','ema_CALM','ema_DEPRESSED','ema_HARM','ema_HOPEFUL','ema_SEEING_THINGS','ema_SLEEPING','ema_SOCIAL','ema_STRESSED','ema_THINK','ema_VOICES','ema_neg_score','ema_pos_score','ema_resp_time_median']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ema = pd.read_csv('full_dataset.csv')\n",
    "ema = ema[['study_id','eureka_id','day','ema_CALM','ema_DEPRESSED','ema_HARM','ema_HOPEFUL','ema_SEEING_THINGS','ema_SLEEPING','ema_SOCIAL','ema_STRESSED','ema_THINK','ema_VOICES','ema_neg_score','ema_pos_score','ema_resp_time_median']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_string = []\n",
    "for day in ema['day']:\n",
    "    days_string.append(str(day))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ema_days = []\n",
    "for day in days_string:\n",
    "    ema_days.append(pd.to_datetime(day))\n",
    "ema['day'] = ema_days\n",
    "ema['ema_score'] = ema['ema_neg_score'] - ema['ema_pos_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ema = ema.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>study_id</th>\n",
       "      <th>eureka_id</th>\n",
       "      <th>day</th>\n",
       "      <th>ema_CALM</th>\n",
       "      <th>ema_DEPRESSED</th>\n",
       "      <th>ema_HARM</th>\n",
       "      <th>ema_HOPEFUL</th>\n",
       "      <th>ema_SEEING_THINGS</th>\n",
       "      <th>ema_SLEEPING</th>\n",
       "      <th>ema_SOCIAL</th>\n",
       "      <th>ema_STRESSED</th>\n",
       "      <th>ema_THINK</th>\n",
       "      <th>ema_VOICES</th>\n",
       "      <th>ema_neg_score</th>\n",
       "      <th>ema_pos_score</th>\n",
       "      <th>ema_resp_time_median</th>\n",
       "      <th>ema_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1</td>\n",
       "      <td>u004</td>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-1</td>\n",
       "      <td>u004</td>\n",
       "      <td>2015-02-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1</td>\n",
       "      <td>u004</td>\n",
       "      <td>2015-02-06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-1</td>\n",
       "      <td>u004</td>\n",
       "      <td>2015-02-13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-1</td>\n",
       "      <td>u004</td>\n",
       "      <td>2015-02-16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    study_id eureka_id        day  ema_CALM  ema_DEPRESSED  ema_HARM  \\\n",
       "6  -1         u004     2015-01-28  0.0       1.0            0.0        \n",
       "13 -1         u004     2015-02-04  1.0       0.0            0.0        \n",
       "15 -1         u004     2015-02-06  1.0       0.0            0.0        \n",
       "22 -1         u004     2015-02-13  1.0       0.0            0.0        \n",
       "25 -1         u004     2015-02-16  3.0       0.0            0.0        \n",
       "\n",
       "    ema_HOPEFUL  ema_SEEING_THINGS  ema_SLEEPING  ema_SOCIAL  ema_STRESSED  \\\n",
       "6   1.0          0.0                0.0           0.0         0.0            \n",
       "13  2.0          0.0                1.0           2.0         2.0            \n",
       "15  2.0          0.0                1.0           2.0         2.0            \n",
       "22  2.0          0.0                0.0           0.0         1.0            \n",
       "25  2.0          0.0                3.0           3.0         0.0            \n",
       "\n",
       "    ema_THINK  ema_VOICES  ema_neg_score  ema_pos_score  ema_resp_time_median  \\\n",
       "6   1.0        0.0         1.0            2.0            0.0                    \n",
       "13  2.0        0.0         2.0            8.0            3.0                    \n",
       "15  2.0        0.0         2.0            8.0            2.0                    \n",
       "22  3.0        0.0         1.0            6.0            5.0                    \n",
       "25  3.0        0.0         0.0            14.0           4.0                    \n",
       "\n",
       "    ema_score  \n",
       "6  -1.0        \n",
       "13 -6.0        \n",
       "15 -6.0        \n",
       "22 -5.0        \n",
       "25 -14.0       "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ema.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished behavior: calllog\n",
      "2020-08-13 00:37:08.474014\n",
      "\n",
      "finished behavior: sms_in\n",
      "2020-08-13 00:37:53.501641\n",
      "\n",
      "finished behavior: sms_out\n",
      "2020-08-13 00:38:37.767464\n",
      "\n",
      "finished behavior: unlock\n",
      "2020-08-13 00:41:33.158025\n",
      "\n",
      "finished behavior: conver\n",
      "2020-08-13 00:42:56.701363\n",
      "\n",
      "finished behavior: sleep\n",
      "2020-08-13 00:45:03.963301\n",
      "\n",
      "finished behavior: act_invehicle\n",
      "2020-08-13 00:46:36.660980\n",
      "\n",
      "finished behavior: act_onbike\n",
      "2020-08-13 00:48:08.627895\n",
      "\n",
      "finished behavior: act_onfoot\n",
      "2020-08-13 00:49:40.426062\n",
      "\n",
      "finished behavior: act_still\n",
      "2020-08-13 00:51:12.300977\n",
      "\n",
      "finished behavior: act_tilting\n",
      "2020-08-13 00:52:44.074957\n",
      "\n",
      "finished behavior: act_unknown\n",
      "2020-08-13 00:54:16.373130\n",
      "\n",
      "finished behavior: light\n",
      "2020-08-13 00:56:22.498901\n",
      "\n",
      "finished behavior: audio_amp\n",
      "2020-08-13 00:58:28.261182\n",
      "\n",
      "finished behavior: audio_noise\n",
      "2020-08-13 01:00:35.209571\n",
      "\n",
      "finished behavior: audio_silence\n",
      "2020-08-13 01:02:41.521439\n",
      "\n",
      "finished behavior: audio_voice\n",
      "2020-08-13 01:04:47.445348\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def get_user_averages(behavior, behavior_name):\n",
    "    user_mean_averages = []\n",
    "#     user_std_averages = []\n",
    "    for u in users:\n",
    "        behavior_user = behavior.loc[behavior.uid == u]\n",
    "        behavior_user = behavior_user.set_index('time')\n",
    "        behavior_user = behavior_user[[behavior_name]]\n",
    "        behavior_user = behavior_user.resample('D').sum()\n",
    "        user_mean_averages.append(behavior_user[behavior_name].mean())\n",
    "#         user_std_averages.append(behavior_user.std())\n",
    "    return user_mean_averages\n",
    "        \n",
    "behaviors = [\n",
    "             calllog,\n",
    "             smslog,\n",
    "             smslog,\n",
    "             unlock, \n",
    "             conver, \n",
    "             sleep, \n",
    "             act,\n",
    "             act,\n",
    "             act,\n",
    "             act,\n",
    "             act,\n",
    "             act,\n",
    "             light, \n",
    "             audio_amp,\n",
    "             audio_infer,\n",
    "             audio_infer,\n",
    "             audio_infer\n",
    "            ]\n",
    "behavior_names = [\n",
    "                  'calllog',\n",
    "                  'sms_in',\n",
    "                  'sms_out',\n",
    "                  'unlock',\n",
    "                  'conver',\n",
    "                  'sleep',\n",
    "                  'act_invehicle',\n",
    "                  'act_onbike',\n",
    "                  'act_onfoot',\n",
    "                  'act_still',\n",
    "                  'act_tilting',\n",
    "                  'act_unknown',\n",
    "                  'light',\n",
    "                  'audio_amp',\n",
    "                  'audio_noise',\n",
    "                  'audio_silence',\n",
    "                  'audio_voice'                  \n",
    "]\n",
    "\n",
    "user_averages_table = pd.DataFrame()\n",
    "for i in range(len(behaviors)):\n",
    "    user_averages_table[behavior_names[i]] = get_user_averages(behaviors[i], behavior_names[i])\n",
    "    print('finished behavior: ' + behavior_names[i])\n",
    "    print(datetime.datetime.now()) \n",
    "    print()\n",
    "user_averages_table.head()\n",
    "user_averages_table.to_csv('feature_table_raw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_averages_table = user_averages_table.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(user_averages_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = user_averages_table.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calllog</th>\n",
       "      <th>sms_in</th>\n",
       "      <th>sms_out</th>\n",
       "      <th>unlock</th>\n",
       "      <th>conver</th>\n",
       "      <th>sleep</th>\n",
       "      <th>act_invehicle</th>\n",
       "      <th>act_onbike</th>\n",
       "      <th>act_onfoot</th>\n",
       "      <th>act_still</th>\n",
       "      <th>act_tilting</th>\n",
       "      <th>act_unknown</th>\n",
       "      <th>audio_noise</th>\n",
       "      <th>audio_silence</th>\n",
       "      <th>audio_voice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.983333</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.923868</td>\n",
       "      <td>3.428139</td>\n",
       "      <td>3.437286</td>\n",
       "      <td>98.218107</td>\n",
       "      <td>117.052036</td>\n",
       "      <td>630.604350</td>\n",
       "      <td>28.648990</td>\n",
       "      <td>1.164023</td>\n",
       "      <td>18.179876</td>\n",
       "      <td>467.360551</td>\n",
       "      <td>42.865767</td>\n",
       "      <td>370.216587</td>\n",
       "      <td>144.062678</td>\n",
       "      <td>933.337819</td>\n",
       "      <td>54.505290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.178633</td>\n",
       "      <td>6.763172</td>\n",
       "      <td>7.993559</td>\n",
       "      <td>129.844611</td>\n",
       "      <td>81.271954</td>\n",
       "      <td>336.543512</td>\n",
       "      <td>24.476459</td>\n",
       "      <td>1.331535</td>\n",
       "      <td>19.578397</td>\n",
       "      <td>193.440992</td>\n",
       "      <td>33.966841</td>\n",
       "      <td>172.310593</td>\n",
       "      <td>155.801855</td>\n",
       "      <td>453.332518</td>\n",
       "      <td>51.701434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100785</td>\n",
       "      <td>3.216136</td>\n",
       "      <td>9.019859</td>\n",
       "      <td>0.011520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.421569</td>\n",
       "      <td>0.236520</td>\n",
       "      <td>72.000245</td>\n",
       "      <td>0.994162</td>\n",
       "      <td>13.914004</td>\n",
       "      <td>0.173235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.827196</td>\n",
       "      <td>0.157818</td>\n",
       "      <td>0.015675</td>\n",
       "      <td>6.071602</td>\n",
       "      <td>45.001525</td>\n",
       "      <td>514.022164</td>\n",
       "      <td>9.088642</td>\n",
       "      <td>0.148452</td>\n",
       "      <td>3.248191</td>\n",
       "      <td>355.496502</td>\n",
       "      <td>15.219772</td>\n",
       "      <td>290.027922</td>\n",
       "      <td>54.944193</td>\n",
       "      <td>730.510198</td>\n",
       "      <td>9.773654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.002481</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>52.606473</td>\n",
       "      <td>111.849944</td>\n",
       "      <td>650.827676</td>\n",
       "      <td>23.688756</td>\n",
       "      <td>0.734484</td>\n",
       "      <td>12.518460</td>\n",
       "      <td>469.326103</td>\n",
       "      <td>37.963569</td>\n",
       "      <td>370.761737</td>\n",
       "      <td>119.208068</td>\n",
       "      <td>1150.081972</td>\n",
       "      <td>53.032266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.869819</td>\n",
       "      <td>2.588275</td>\n",
       "      <td>2.292217</td>\n",
       "      <td>122.995588</td>\n",
       "      <td>154.035807</td>\n",
       "      <td>837.752445</td>\n",
       "      <td>43.768307</td>\n",
       "      <td>1.625520</td>\n",
       "      <td>24.576137</td>\n",
       "      <td>622.315433</td>\n",
       "      <td>63.420362</td>\n",
       "      <td>433.175607</td>\n",
       "      <td>165.964734</td>\n",
       "      <td>1244.564403</td>\n",
       "      <td>85.457769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>136.202750</td>\n",
       "      <td>36.717949</td>\n",
       "      <td>49.369231</td>\n",
       "      <td>512.678133</td>\n",
       "      <td>384.847222</td>\n",
       "      <td>1360.732759</td>\n",
       "      <td>90.458376</td>\n",
       "      <td>5.995587</td>\n",
       "      <td>78.235507</td>\n",
       "      <td>777.994895</td>\n",
       "      <td>133.991692</td>\n",
       "      <td>1378.416667</td>\n",
       "      <td>763.488067</td>\n",
       "      <td>1430.697691</td>\n",
       "      <td>198.744379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          calllog     sms_in    sms_out      unlock      conver        sleep  \\\n",
       "count  0.983333    59.000000  59.000000  0.983333    0.983333    0.983333      \n",
       "mean   10.923868   3.428139   3.437286   98.218107   117.052036  630.604350    \n",
       "std    19.178633   6.763172   7.993559   129.844611  81.271954   336.543512    \n",
       "min    0.000000    0.008889   0.000000   0.100785    3.216136    9.019859      \n",
       "25%    0.827196    0.157818   0.015675   6.071602    45.001525   514.022164    \n",
       "50%    5.002481    0.882353   0.387755   52.606473   111.849944  650.827676    \n",
       "75%    15.869819   2.588275   2.292217   122.995588  154.035807  837.752445    \n",
       "max    136.202750  36.717949  49.369231  512.678133  384.847222  1360.732759   \n",
       "\n",
       "       act_invehicle  act_onbike  act_onfoot   act_still  act_tilting  \\\n",
       "count  0.983333       0.983333    0.983333    0.983333    0.983333      \n",
       "mean   28.648990      1.164023    18.179876   467.360551  42.865767     \n",
       "std    24.476459      1.331535    19.578397   193.440992  33.966841     \n",
       "min    0.011520       0.000000    0.000000    47.421569   0.236520      \n",
       "25%    9.088642       0.148452    3.248191    355.496502  15.219772     \n",
       "50%    23.688756      0.734484    12.518460   469.326103  37.963569     \n",
       "75%    43.768307      1.625520    24.576137   622.315433  63.420362     \n",
       "max    90.458376      5.995587    78.235507   777.994895  133.991692    \n",
       "\n",
       "       act_unknown  audio_noise  audio_silence  audio_voice  \n",
       "count  0.983333     0.983333     0.983333       0.983333     \n",
       "mean   370.216587   144.062678   933.337819     54.505290    \n",
       "std    172.310593   155.801855   453.332518     51.701434    \n",
       "min    72.000245    0.994162     13.914004      0.173235     \n",
       "25%    290.027922   54.944193    730.510198     9.773654     \n",
       "50%    370.761737   119.208068   1150.081972    53.032266    \n",
       "75%    433.175607   165.964734   1244.564403    85.457769    \n",
       "max    1378.416667  763.488067   1430.697691    198.744379   "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "periods_columns = [c for c in user_averages_table.columns if 'sms' not in c and 'light' not in c and 'audio_amp' not in c]\n",
    "for b in periods_columns:\n",
    "    summary[b] = summary[b]/60.0\n",
    "summary[[c for c in summary.columns if 'light' not in c and 'audio_amp' not in c]]"
   ]
  }
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